A method, system, device and storage medium for classifying matrix productivity of a glutenite reservoir based on electrical imaging data

Abstract

The application discloses a kind of based on electrical imaging data's sandy conglomerate reservoir matrix productivity classification method, including steps: according to sandy conglomerate reservoir matrix electrical imaging logging data, on the basis of the morphology gradient of image, extract conglomerate content in sandy conglomerate reservoir by watershed segmentation algorithm;According to the oil test data, the oil production of the oil test interval is determined, various parameters that affect the reservoir oil production are analyzed, and the key parameters of main control are optimized;According to the main control key parameter, the comprehensive quality index indicating the reservoir property and the sandy index indicating the lithology change are constructed;The oil test reservoir is divided into three categories by using the oil production, the corresponding relationship of the comprehensive quality index of reservoir and the sandy index is obtained by establishing the two-dimensional coordinate relationship chart of the comprehensive quality index of reservoir, the sandy index and three types of reservoirs, and the productivity classification of sandy conglomerate reservoir matrix is realized.

Description

Technical Field

[0001] This invention belongs to the field of well logging evaluation technology for sandstone and conglomerate oil and gas reservoirs, specifically relating to a method, system, and storage medium for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data. Background Technology

[0002] Due to variations in internal sedimentary characteristics and structures, sandstone and conglomerate reservoirs exhibit complex and diverse geological features, fluid properties, and pore structures, resulting in significant differences in single-well production capacity influenced by multiple factors. Identifying the factors affecting production capacity is crucial for the matrix production capacity classification and evaluation of sandstone and conglomerate reservoirs. Classifying reservoir production capacity based on a clear understanding of these influencing factors provides vital technical support for well logging interpretation and production commissioning in sandstone and conglomerate reservoirs.

[0003] In recent years, complex lithological reservoirs have gradually become a focus of exploration and development. Compared with conventional sandstone reservoirs, conglomerate reservoirs are characterized by strong heterogeneity, mixing of sand, mud, and gravel, high clay content, complex pore structure, poor physical properties, and indistinct logging curve response characteristics. Various factors simultaneously affect the reservoir's production capacity. Currently, there are several reservoir productivity classification methods, mainly based on conventional logging curve modeling, using macroscopic parameters and experimental analysis of microscopic parameters to classify reservoir productivity. These methods primarily rely on conventional logging data and experimental analysis data, making parameter extraction difficult and focusing on qualitative identification. They have some classification effect in reservoirs with high porosity, weak heterogeneity, and uniform lithology. With the continuous deepening of exploration and development, facing complex lithology and diverse factors influencing productivity in conglomerate reservoirs, the previous methods based on conventional logging data and single-factor evaluation of reservoir productivity are no longer suitable for the current exploration and development of conglomerate reservoirs in oilfields. Therefore, it is necessary to establish a universal matrix productivity classification method for conglomerate reservoirs based on multi-factor analysis. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the present invention aims to provide a method, system and storage medium for classifying the productivity of sandstone and conglomerate reservoir matrix based on electrical imaging data, so as to solve the problem that it is difficult to accurately evaluate the productivity of sandstone and conglomerate reservoir matrix with complex lithology and diverse factors affecting productivity based on conventional logging data.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] This invention discloses a method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data, comprising the following steps:

[0007] 1) Based on the matrix electrical imaging logging data of sandstone and conglomerate reservoirs, the gravel content in the sandstone and conglomerate reservoirs is extracted using a watershed segmentation algorithm based on the morphological gradient of the images.

[0008] 2) Determine the rice production of the test reservoir based on the test oil data, analyze various parameters affecting the rice production of the reservoir, and select the key parameters that are the main control parameters;

[0009] 3) Based on the main control parameters, construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes;

[0010] 4) The test reservoirs were divided into three categories using the rice-produced liquid. By establishing a two-dimensional coordinate relationship chart of the reservoir comprehensive quality index, sand quality index and the three types of reservoirs, the corresponding relationship between the reservoir comprehensive quality index and sand quality index was obtained, and the matrix productivity classification of sandstone and conglomerate reservoirs was realized.

[0011] Preferably, in step 1), the morphological gradient of the image is obtained on the electro-imaging grayscale image using equation (1) based on the electro-imaging logging data:

[0012]

[0013] In the formula, Let δ(I) represent the morphological gradient of the image, where δ(I) represents the dilation operation on image I using the structuring element, and ε(I) represents the erosion operation on image I using the structuring element.

[0014] Preferably, in step 1), the number of gravel in the sandstone and conglomerate is obtained by segmenting the image based on the image gradient using the watershed segmentation algorithm (2), and finally the overall gravel content V of the image is calculated using equation (3). ls :

[0015]

[0016]

[0017] In the formula, V ls Here, denoted as gravel content (%), A represents the area of ​​the electro-imaging image processing window, N represents the number of gravels within the electro-imaging image processing window, and Bi represents the area of ​​the i-th gravel within the electro-imaging image processing window.

[0018] Preferably, in step 2), the rice yield of the test layer is determined according to formula (4) based on the test oil data:

[0019]

[0020] In the formula, Y represents the liquid yield per meter, in m³. 3 / m;V 油 The volume of crude oil in the test layer is expressed in cubic meters (m³). 3 ;ρ 油 Crude oil density in the region, in g / m³ 3 ;ρ 水 The density of formation water in the region, in g / m³3 V 水 The volume of formation water in the test section is expressed in cubic meters (m³). 3 H represents the thickness of the sandstone and conglomerate in the test section, in meters.

[0021] Preferably, the key parameters for main control are acoustic porosity, neutron porosity, average capillary radius, clay content, gravel content, mud content, and cementation type.

[0022] Preferably, in step 3), based on the main control key parameters, a porosity quality factor is constructed using equation (5), a comprehensive quality index J indicating reservoir properties is constructed using equation (6), and a sandy index Z indicating lithological changes is constructed using equation (7).

[0023]

[0024]

[0025] Z = 100 - V sh -V ls (7)

[0026] In the formula, a is the bonding type coefficient; B e φ is the porosity quality factor, a decimal. A Acoustic porosity, in %; φ C Neutron porosity, in %; r is the average capillary radius, in μm, calculated from experimental analysis data; V cl Clay content, expressed as a percentage, calculated using a regional empirical formula; V ls Gravel content, in %; V sh The content is mud content, expressed as a percentage, calculated using natural gamma conventional methods.

[0027] Preferably, in step 4), the test reservoir is divided into three categories using rice-produced liquid, with a daily production greater than 8m³. 3 The reservoir with a daily production of 2-8 m³ is classified as a Class I reservoir. 3 The reservoir with a daily production of / m is a Class II reservoir, with a daily production of less than 2m. 3 The reservoirs with a density of / m are classified as Class III reservoirs;

[0028] The corresponding relationship between the reservoir comprehensive quality index and the sand quality index is as follows: when J>13 and Z>74, it is a Class I reservoir; when 0.62≤J≤13 and 40≤Z≤74, it is a Class II reservoir; when J<0.62 and Z<40, it is a Class III reservoir.

[0029] This invention also discloses a matrix productivity classification system for sandstone and conglomerate reservoirs based on electrical imaging data, comprising:

[0030] 1) Establish a gravel content module in sandstone and conglomerate reservoirs: This module is used to extract the gravel content in sandstone and conglomerate reservoirs based on the morphological gradient of the image using a watershed segmentation algorithm, according to the matrix electrical imaging logging data of sandstone and conglomerate reservoirs.

[0031] 2) Establish the key parameter module for main control: It is used to determine the rice production of the test reservoir based on the test oil data, analyze various parameters affecting the rice production of the reservoir, and select the key parameters for main control.

[0032] 3) Establish modules for a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes: These modules are used to construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes based on the main control parameters.

[0033] 4) Establish a module for the correspondence between the comprehensive reservoir quality index and the sand quality index: This module is used to classify the test reservoir into three categories using the produced liquid. By establishing a two-dimensional coordinate relationship chart of the comprehensive reservoir quality index, the sand quality index, and the three types of reservoirs, the correspondence between the comprehensive reservoir quality index and the sand quality index is obtained, thereby realizing the classification of the matrix productivity of sandstone and conglomerate reservoirs.

[0034] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the classification methods described above.

[0035] The present invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the classification method described in any of the preceding claims.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention provides a method for classifying the productivity of sandstone and conglomerate reservoir matrix based on electrical imaging data. By analyzing electrical imaging data, conventional curves, and experimental data, sensitive parameters for reservoir productivity are determined. A sand quality index indicating reservoir lithological changes and a comprehensive quality index indicating reservoir physical properties are constructed. A two-dimensional rectangular coordinate system chart is established between the sand quality index and the comprehensive physical property quality index and the three types of reservoirs. The correspondence between the sand quality index and the comprehensive quality index and reservoir productivity is then quantitatively determined. For sandstone and conglomerate reservoirs, the productivity classification of the sandstone and conglomerate reservoir matrix can be achieved quickly, quantitatively, and accurately through the relationship formula. This solves the shortcomings of traditional methods that rely on a single influencing factor to evaluate the productivity of sandstone and conglomerate reservoir matrix, which is complex in lithology and has diverse factors affecting productivity. These methods are inaccurate, lack universality, and are limited in qualitative identification. For untested wells, the accuracy of interpretation and evaluation can be improved based on the location of the reservoir chart, and production plans can be optimized, thus having greater application value.

[0038] Furthermore, the morphological gradient of the image is obtained through equation (1). The gradient image can better highlight the contours of different rock components in the electro-imaging image and clearly reflect the changing trend of the image.

[0039] Furthermore, based on the image gradient, the number of gravels in the sandstone and conglomerate is obtained by segmentation processing. Finally, the overall gravel content of the image is calculated by equation (3). From qualitatively identifying the amount of gravel using electrical imaging data to quantitatively and accurately calculating the gravel content of the reservoir, accurate parameters can be provided for reservoir evaluation.

[0040] Furthermore, based on the oil test data, the per-meter production of the tested reservoir section is determined by formula (4) to prepare for the analysis of various reservoir parameters and actual production. Since large-scale oil testing of the reservoir may involve the coexistence of sandstone and mudstone, and mudstone is not a reservoir, the per-meter production of the tested reservoir section can be calculated using the thickness of sandstone and mudstone, which can more accurately reflect the production capacity of the reservoir.

[0041] Furthermore, a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological variations were constructed. A higher comprehensive quality index indicates better overall matrix properties of the reservoir, while a higher sandy index indicates higher sand content. These two indices essentially cover different sandstone and conglomerate reservoirs with differences in cementation type, pore structure, and lithology, quantitatively characterizing the matrix conditions of various sandstone and conglomerate reservoirs under different sedimentary environments. Ultimately, a comprehensive classification method for reservoir productivity was formed, overcoming the difficulty in determining productivity caused by the strong heterogeneity, complex pore structure, and large lithological variations of sandstone and conglomerate reservoirs, which greatly interfere with reservoir productivity. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data according to the present invention.

[0043] Figure 2 The image shows the results of gravel content extraction in this invention; where (a) is the original electro-imaging image of the sandstone and conglomerate reservoir, with white representing gravel, and (b) is a diagram showing the gravel composition of the reservoir after processing, with black representing gravel.

[0044] Figure 3 This is a graph showing the correlation coefficients between reservoir parameters and liquid production capacity in this invention.

[0045] Figure 4 This is a coordinate graph showing the sand quality index and the comprehensive quality index of this invention. Detailed Implementation

[0046] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0047] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0048] like Figure 1 As shown, a method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data includes the following steps:

[0049] S101: Based on the matrix electrical imaging logging data of sandstone and conglomerate reservoirs, the gravel content in sandstone and conglomerate reservoirs is extracted by watershed segmentation algorithm based on the image gradient.

[0050] S102: Determine the liquid yield of the test reservoir section based on the test oil data, analyze various parameters affecting the liquid yield of the reservoir, and select the key parameters that are the main control parameters.

[0051] S103: Based on the main control key parameters, construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes;

[0052] S104: Using rice-produced liquid, the test reservoirs are divided into three categories. By establishing a two-dimensional coordinate relationship chart of the reservoir comprehensive quality index, sand quality index and the three types of reservoirs, the corresponding relationship of the reservoir comprehensive quality index and sand quality index is obtained, and the matrix productivity classification of sandstone and conglomerate reservoirs is realized.

[0053] Step 1: Select electrical imaging logging data of sandstone and conglomerate reservoir matrix in a certain area, and perform multi-scale morphological gradient transformation on the electrical imaging grayscale image:

[0054]

[0055] In the formula, Let δ(I) represent the morphological gradient of the electro-imaging image I, where δ(I) represents the dilation operation on image I using the structuring element, and ε(I) represents the erosion operation on image I using the structuring element.

[0056] Based on the image gradient, a watershed segmentation algorithm is used for segmentation, which can effectively distinguish gravel from other minerals in the electro-imaging image. The bright white gravel component is quantitatively extracted, and the number of sandstone and conglomerate gravels within the electro-imaging window is obtained. (See [link to documentation]). Figure 2 Finally, the overall gravel content V of the image was calculated. ls :

[0057]

[0058]

[0059] In the formula, V ls Here, denoted as gravel content (%), A represents the area of ​​the electro-imaging image processing window, N represents the number of gravels within the electro-imaging image processing window, and Bi represents the area of ​​the i-th gravel within the electro-imaging image processing window.

[0060] Step 2: Based on single-well oil testing data, the reservoir crude oil production is converted into water volume according to the regional oil-water density. Combined with the reservoir water production, the overall reservoir production is calculated. Finally, the per-meter production of the tested section is calculated based on the thickness of the sandstone and conglomerate reservoir. Various sensitive parameters affecting the per-meter production of the reservoir are analyzed from aspects such as reservoir lithology, clay content, gravel content, cementation type, and micropore structure. (See [link to relevant documentation]). Figure 3 The figure shows the analysis results of various parameters and rice production liquid. The numbers represent correlation coefficients. Based on the magnitude of the correlation coefficients, acoustic porosity, neutron porosity, average capillary radius, clay content, gravel content, mud content, and cementation type were selected as the key parameters for main control.

[0061]

[0062] In the formula, Y represents the liquid yield per meter, in m³. 3 / m;V 油 The volume of crude oil in the test layer is expressed in cubic meters (m³). 3 ;ρ 油 Crude oil density in the region, in g / m³ 3 ;ρ 水 The density of formation water in the region, in g / m³ 3 V 水 The volume of formation water in the test section is expressed in cubic meters (m³). 3 H represents the thickness of the sandstone and conglomerate in the test section, in meters.

[0063] Step 3: Based on the key parameters controlling reservoir productivity, a porosity quality factor is established using two parameters: acoustic matrix porosity and neutron total porosity, representing the development of reservoir matrix pores; average capillary radius represents the reservoir pore structure; clay and gravel content represents the reservoir pore development environment; and different cementation types also reflect the quality of reservoir physical properties. Finally, a comprehensive quality index J indicating reservoir physical properties is constructed. The main rock components of sandstone and conglomerate are mudstone, gravel, and sandstone. The proportion of these three lithologies directly affects reservoir productivity. Combining mud content and gravel content extracted by electrophysiological imaging, a sandy index Z indicating lithological changes is constructed.

[0064]

[0065]

[0066] Z = 100 - V sh -V ls (7)

[0067] In the formula, 'a' is the cementation type coefficient, which is 1.5 for argillaceous cementation and 0.5 for calcareous cementation; B e Pore ​​quality factor, in decimal units; φ A Acoustic porosity, in %; φ C Neutron porosity, in %; V represents the average capillary radius, in μm, calculated from experimental analysis data. cl Clay content, expressed as a percentage, calculated using a regional empirical formula; V ls Gravel content, in %; V sh The content is mud content, expressed as a percentage, calculated using natural gamma conventional methods.

[0068] Step 4: Using rice-based liquid production, the test reservoir was divided into three categories: circular reservoirs with a daily production greater than 8 m³. 3 A type of reservoir with a daily production of 2-8 m³ / m³, square-shaped reservoirs. 3 / m Class II reservoir, rhombus shape indicates daily production less than 2m 3 For the three types of reservoirs at / m, establish a comprehensive reservoir quality index, sand content index, and a two-dimensional coordinate relationship chart for the three types of reservoirs. See [link / reference]. Figure 4 In the figure, the decimals represent the liquid production per meter. The vertical axis, sand quality index, represents the lithological variation of the reservoir; a higher sand quality index indicates a higher sand content. The horizontal axis, comprehensive quality index, represents the quality of the reservoir matrix. Some reservoirs have high sand content, but their overall productivity is not good due to the influence of pore structure and cementation type. The final relationship between the comprehensive quality index and sand quality index is established: Class I reservoirs: J>13, Z>74; Class II reservoirs: 0.62≤J≤13, 40≤Z≤74; Class III reservoirs: J<0.62, Z<40. This ultimately forms the classification method for the productivity of sandstone and conglomerate reservoir matrix.

[0069] This invention also provides a matrix productivity classification system for sandstone and conglomerate reservoirs based on electrical imaging data, comprising:

[0070] 1) Establish a gravel content module in sandstone and conglomerate reservoirs: This module is used to extract the gravel content in sandstone and conglomerate reservoirs based on the morphological gradient of the image using a watershed segmentation algorithm, according to the matrix electrical imaging logging data of sandstone and conglomerate reservoirs.

[0071] 2) Establish the key parameter module for main control: It is used to determine the rice production of the test reservoir based on the test oil data, analyze various parameters affecting the rice production of the reservoir, and select the key parameters for main control.

[0072] 3) Establish modules for a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes: These modules are used to construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes based on the main control parameters.

[0073] 4) Establish a module for the correspondence between the comprehensive reservoir quality index and the sand quality index: This module is used to classify the test reservoir into three categories using the produced liquid. By establishing a two-dimensional coordinate relationship chart of the comprehensive reservoir quality index, the sand quality index, and the three types of reservoirs, the correspondence between the comprehensive reservoir quality index and the sand quality index is obtained, thereby realizing the classification of the matrix productivity of sandstone and conglomerate reservoirs.

[0074] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described classification method. The computer storage medium can be any available medium or data storage device accessible by a computer, including magnetic storage devices (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage devices (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage devices (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs), etc.).

[0075] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the classification method described above. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0076] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data, characterized in that, Includes the following steps: 1) Based on the matrix electrical imaging logging data of sandstone and conglomerate reservoirs, the gravel content in the sandstone and conglomerate reservoirs is extracted by watershed segmentation algorithm based on the morphological gradient of the image. 2) Determine the rice production of the test reservoir based on the test oil data, analyze various parameters affecting the rice production of the reservoir, and select the key parameters that are the main control parameters; 3) Based on the main control parameters, construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes; 4) The test reservoirs were divided into three categories using rice-produced liquid. By establishing a two-dimensional coordinate relationship chart of the reservoir comprehensive quality index, sand quality index and the three types of reservoirs, the corresponding relationship of the reservoir comprehensive quality index and sand quality index was obtained, and the matrix productivity classification of sandstone and conglomerate reservoirs was realized. The key parameters selected for primary control are acoustic porosity, neutron porosity, average capillary radius, clay content, gravel content, mud content, and cementation type. In step 3), based on the key control parameters, the porosity quality factor is constructed using equation (5), and the comprehensive quality index indicating reservoir properties is constructed using equation (6). A sandy index indicating lithological changes is constructed using equation (7). : （5） （6） （7） In the formula, This is the bonding type coefficient; Pore ​​quality factor, decimal; Acoustic porosity, unit ; Neutron porosity, unit ; The average capillary radius, in units Calculated from experimental analysis data; Clay content, unit Calculated using regional empirical formulas; Gravel content, unit ; The content of clay is expressed in units of... Calculated using natural gamma conventional methods; In step 4), the tested oil reservoir is divided into three categories using rice-produced liquid, with a daily production greater than 8... It is classified as a type of reservoir, with a daily production of 2-8 It is a Class II reservoir with a daily production of less than 2 The reservoirs are classified into three types. The corresponding relationship between the reservoir comprehensive quality index and the sand quality index is as follows: when J>13 and Z>74, it is a Class I reservoir; when 0.62≤J≤13 and 40≤Z≤74, it is a Class II reservoir; when J<0.62 and Z<40, it is a Class III reservoir.

2. The method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data according to claim 1, characterized in that, In step 1), the morphological gradient of the image is obtained using equation (1) based on the electrical imaging logging data: （1） In the formula, Represents the morphological gradient of an image. This indicates the use of structuring elements to manipulate images. Perform an expansion operation; This indicates the use of structuring elements to manipulate images. Perform corrosion operation.

3. The method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data according to claim 1, characterized in that, In step 1), the number of gravels in the sandstone and conglomerate is obtained by segmenting the image based on the morphological gradient using the watershed segmentation algorithm (2). Finally, the gravel content is calculated using equation (3). : （2） （3） In the formula, Gravel content, unit , The area of ​​the electro-imaging image processing window; The number of gravels within the electro-imaging image processing window; The first one in the electro-imaging image processing window The area of ​​each gravel.

4. The method for classifying the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data according to claim 1, characterized in that, In step 2), the yield of the tested reservoir is determined using equation (4) based on the oil test data: （4） In the formula, For rice-based liquid production, unit ; The volume of crude oil in the test layer is expressed in units. ; Regional crude oil density, unit ; The density of formation water in the region, in units ; The volume of formation water in the test oil layer is expressed in units of... ; The thickness of the sandstone and conglomerate in the test section is given in units of... .

5. A classification system for the matrix productivity of sandstone and conglomerate reservoirs based on electrical imaging data, which employs the classification method described in claim 1, characterized in that, include: 1) Establish a gravel content module in sandstone and conglomerate reservoirs: This module is used to extract the gravel content in sandstone and conglomerate reservoirs based on the morphological gradient of the image by using a watershed segmentation algorithm, according to the matrix electrical imaging logging data of sandstone and conglomerate reservoirs. 2) Establish the key parameter module for main control: This module is used to determine the rice production of the test reservoir based on the test data, analyze various parameters that affect the rice production of the reservoir, and select the key parameters for main control. 3) Establish modules for a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes: These modules are used to construct a comprehensive quality index indicating reservoir properties and a sandy index indicating lithological changes based on the main control parameters. 4) Establish a module for the correspondence between the comprehensive reservoir quality index and the sand quality index: This module is used to classify the test reservoir into three categories using the produced liquid. By establishing a two-dimensional coordinate relationship chart of the comprehensive reservoir quality index, the sand quality index, and the three types of reservoirs, the correspondence between the comprehensive reservoir quality index and the sand quality index is obtained, thereby realizing the classification of the matrix productivity of sandstone and conglomerate reservoirs.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the classification method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the classification method according to any one of claims 1 to 4.

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